A recent study from the Cambridge Centre for Alternative Finance (CCAF) analyzed the current state of AI adoption in financial services, as well as its subsequent implications. The pace of AI application in financial services is clearly accelerating as companies begin to leverage AI to increase profitability and achieve scale. This has complicated and multifaceted implications and repercussions.
The overarching findings of the study suggest that AI is expected to transform a number of different paradigms within the financial services industry. These anticipated changes include how data is utilized to generate more actionable insights; business model innovation (e.g., selling AI as a service); changes to the competitive environment with the entrance of “bigtech” and consolidation; various impacts on jobs and regulation; impacts on risks and biases; and the further development and adoption of game-changing technologies.
The key findings of this empirical study are as follows:
- AI is expected to turn into an essential business driver across the financial services industry in the short run, with 77% of all respondents anticipating AI to possess high or very high overall importance to their businesses within two years. While AI is currently perceived to have reached a higher strategic relevance to fintechs, incumbents are aspiring to catch up within two years.
- The rising importance of AI is accompanied by the increasingly broad adoption of AI across key business functions. Approximately 64% of surveyed respondents anticipate employing AI in all of the following categories – generating new revenue potential through new products and processes, process automation, risk management, customer service and client acquisition – within the next two years. Only 16% of respondents currently employ AI in all of these areas.
- Risk management is the usage domain with the highest current AI implementation rates (56%), followed by the generation of new revenue potential through new AI-enabled products and processes, adopted by 52%. However, firms expect the latter to become the most important usage area within two years.
- AI is expected to become a key lever of success for specific financial services sectors. For example, it is expected to turn into a major driver of investment returns for asset managers. Lenders widely expect to profit from leveraging AI in AI-enabled credit analytics, while payment providers anticipate expanding their AI usage profile towards harnessing AI for customer service and risk management.
- With the race to AI leadership, the technological gap between high and low spenders is widening as high spenders plan to further increase their R&D investments. These spending ambitions appear to be driven by more-than-linear increases in pay-offs from investing in AI, which are shown to come into effect once AI investment has reached a “critical” mass of approximately 10% R&D expenditure.
- Fintechs appear to be using AI differently compared to incumbents. A higher share of fintechs tends to create AI-based products and services, employ autonomous decision-making systems, and rely on cloud-based offerings. Incumbents predominantly focus on harnessing AI to improve existing products. This might explain why AI appears to have a higher positive impact on fintechs’ profitability, with 30% indicating a significant AI-induced increase in profitability compared to 7% of incumbents.
- Fintechs are more widely selling AI-enabled products as a service. Successful real-world implementations demonstrate that selling AI as a service may allow large organizations to create “AI flywheels” – self-enforcing virtuous circles – through offering improved AI-driven services based on larger and more diverse datasets and attracting talent.
- AI leaders generally build dedicated corporate resources for AI implementation and oversight – mainly a data analytics function – to work with their existing IT department. On average, they also use more sophisticated technology to empower more complex AI use cases.
- Leveraging alternative datasets to generate novel insights is a key part of harnessing the benefits of AI with 60% of all respondents utilizing new or alternative forms of data in AI applications. The most frequently used alternative data sources include social media, data from payment providers, and geo-location data.
- Incumbents expect AI to replace nearly 9% of all jobs in their organization by 2030, while fintechs anticipate AI to expand their workforce by 19%. Within the surveyed sample, this implies an estimated net reduction of approximately 336,000 jobs in incumbents and an increase of 37,700 jobs in fintechs. Reductions are expected to be highest in Investment Management, with participants anticipating a net decrease of 10% within 5 years and 24% within 10 years.
- Regardless of sectors and entity types, quality of and access to data and access to talent are considered to be major obstacles to implementing AI. Each of these factors is perceived to be a hurdle by more than 80% of all respondents, whereas aspects like the cost of hardware and software, market uncertainty, and technological maturity appear to represent lesser hindrances.
- Almost 40% of all respondents feel that regulation hinders their implementation of AI, whereas just over 30% perceive that regulation facilitates or enables it. Organizations feel most impeded by data sharing regulations between jurisdictions and entities, but many also deem regulatory complexity and uncertainty to be burdensome. Firms’ assessments of the impact of regulation tend to be more positive in China than in the US, the UK, or mainland Europe.
- Mass AI adoption is expected to exacerbate certain market-wide risks and biases, and at least one in five firms do not believe they are well placed to mitigate those. Firms are particularly wary of the potential for AI to entrench biases in decision-making, or to expose them, through shared resources, to mass data and privacy breaches. Nevertheless, many firms are involving Risk and Compliance teams in AI implementation, and those who do tend to be more confident in their risk mitigation capability as a result.
- Long-established, simple machine learning algorithms are more widely used than complex solutions. Nonetheless, a large share of respondents is planning to implement Natural Language Processing (NLP) and Computer Vision, which commonly involve Deep Learning, within two years.
- Nearly half of all participants regard “bigtech” leveraging AI capabilities to enter financial services as a major competitive threat.